liuhaotian

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LLaVA-Lightning-MPT-7B-preview

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Hi @thefaheem , you can simply pull the latest code base and set --model-name to liuhaotian/LLaVA-Lightning-MPT-7B-preview.

It will download and load the model automatically.

This is What I Asked You in Your Twitter DM. Anyways Thanks For Effort and Support.

Your Work inspires Me amd it's Really Amazing @liuhaotian

This is What I Got when I initialise the model with device map with "auto"

/content/llava/model/mpt/attention.py:148: UserWarning: Using attn_impl: torch. If your model does not use alibi or prefix_lm we recommend using attn_impl: flash otherwise we recommend using attn_impl: triton.
warnings.warn('Using attn_impl: torch. If your model does not use alibi or ' + 'prefix_lm we recommend using attn_impl: flash otherwise ' + 'we recommend using attn_impl: triton.')
Some weights of the model checkpoint at openai/clip-vit-large-patch14 were not used when initializing CLIPVisionModel: ['text_model.encoder.layers.0.layer_norm2.weight', 'text_model.encoder.layers.5.layer_norm2.bias', 'text_model.encoder.layers.6.mlp.fc1.bias', 'text_model.encoder.layers.1.self_attn.v_proj.weight', 'text_model.encoder.layers.10.layer_norm2.weight', 'text_model.encoder.layers.5.self_attn.q_proj.weight', 'text_model.encoder.layers.7.self_attn.q_proj.weight', 'text_model.encoder.layers.5.self_attn.out_proj.bias', 'text_model.encoder.layers.6.self_attn.q_proj.bias', 'text_model.encoder.layers.5.layer_norm2.weight', 'text_model.encoder.layers.6.mlp.fc2.bias', 'text_projection.weight', 'text_model.encoder.layers.9.layer_norm1.bias', 'text_model.encoder.layers.11.layer_norm1.bias', 'text_model.encoder.layers.9.self_attn.k_proj.weight', 'text_model.encoder.layers.3.self_attn.k_proj.weight', 'text_model.encoder.layers.7.self_attn.v_proj.bias', 'text_model.encoder.layers.0.self_attn.k_proj.weight', 'text_model.encoder.layers.2.layer_norm2.weight', 'text_model.encoder.layers.5.self_attn.out_proj.weight', 'text_model.encoder.layers.2.self_attn.v_proj.weight', 'text_model.encoder.layers.8.self_attn.out_proj.bias', 'text_model.encoder.layers.11.self_attn.v_proj.bias', 'text_model.encoder.layers.2.self_attn.k_proj.weight', 'text_model.encoder.layers.4.self_attn.v_proj.bias', 'text_model.encoder.layers.3.mlp.fc1.weight', 'text_model.encoder.layers.7.mlp.fc1.weight', 'text_model.encoder.layers.0.mlp.fc2.weight', 'text_model.encoder.layers.7.mlp.fc1.bias', 'text_model.encoder.layers.1.mlp.fc2.weight', 'text_model.encoder.layers.6.layer_norm2.weight', 'text_model.encoder.layers.1.self_attn.out_proj.bias', 'text_model.encoder.layers.8.self_attn.k_proj.weight', 'text_model.encoder.layers.4.self_attn.k_proj.weight', 'text_model.encoder.layers.1.layer_norm1.weight', 'text_model.encoder.layers.5.mlp.fc2.weight', 'text_model.encoder.layers.11.mlp.fc1.bias', 'text_model.encoder.layers.7.layer_norm1.weight', 'text_model.encoder.layers.4.mlp.fc1.bias', 'text_model.encoder.layers.9.self_attn.v_proj.bias', 'text_model.encoder.layers.10.self_attn.q_proj.weight', 'text_model.encoder.layers.8.self_attn.k_proj.bias', 'text_model.encoder.layers.11.self_attn.q_proj.bias', 'text_model.encoder.layers.8.self_attn.v_proj.bias', 'text_model.encoder.layers.11.self_attn.q_proj.weight', 'text_model.encoder.layers.1.self_attn.q_proj.weight', 'text_model.encoder.layers.2.self_attn.out_proj.bias', 'text_model.encoder.layers.8.self_attn.out_proj.weight', 'text_model.encoder.layers.4.mlp.fc2.weight', 'text_model.encoder.layers.2.self_attn.q_proj.weight', 'text_model.encoder.layers.3.layer_norm1.weight', 'text_model.encoder.layers.3.self_attn.k_proj.bias', 'text_model.encoder.layers.1.self_attn.q_proj.bias', 'text_model.encoder.layers.3.mlp.fc2.weight', 'text_model.encoder.layers.5.layer_norm1.weight', 'text_model.encoder.layers.8.layer_norm1.weight', 'text_model.encoder.layers.3.self_attn.q_proj.weight', 'text_model.encoder.layers.0.self_attn.k_proj.bias', 'text_model.encoder.layers.4.mlp.fc2.bias', 'text_model.encoder.layers.5.self_attn.k_proj.bias', 'text_model.encoder.layers.7.self_attn.q_proj.bias', 'text_model.encoder.layers.0.self_attn.v_proj.weight', 'text_model.encoder.layers.6.layer_norm2.bias', 'text_model.encoder.layers.3.self_attn.out_proj.weight', 'text_model.encoder.layers.10.self_attn.k_proj.weight', 'text_model.encoder.layers.6.self_attn.v_proj.bias', 'text_model.encoder.layers.0.layer_norm1.weight', 'text_model.encoder.layers.8.layer_norm2.bias', 'text_model.encoder.layers.5.layer_norm1.bias', 'text_model.encoder.layers.7.self_attn.out_proj.weight', 'text_model.encoder.layers.6.self_attn.out_proj.weight', 'text_model.encoder.layers.11.self_attn.k_proj.weight', 'text_model.encoder.layers.1.self_attn.k_proj.weight', 'text_model.encoder.layers.10.layer_norm1.bias', 'text_model.encoder.layers.1.layer_norm2.weight', 'text_model.encoder.layers.9.self_attn.q_proj.bias', 'text_model.encoder.layers.9.layer_norm2.bias', 'text_model.encoder.layers.11.mlp.fc2.weight', 'text_model.encoder.layers.9.layer_norm1.weight', 'text_model.final_layer_norm.weight', 'text_model.encoder.layers.3.layer_norm1.bias', 'text_model.encoder.layers.5.self_attn.q_proj.bias', 'text_model.encoder.layers.2.mlp.fc1.bias', 'text_model.encoder.layers.11.layer_norm1.weight', 'text_model.encoder.layers.7.mlp.fc2.bias', 'text_model.encoder.layers.10.self_attn.v_proj.bias', 'text_model.encoder.layers.1.self_attn.out_proj.weight', 'text_model.encoder.layers.7.self_attn.k_proj.weight', 'text_model.encoder.layers.10.mlp.fc1.weight', 'text_model.encoder.layers.4.mlp.fc1.weight', 'text_model.encoder.layers.9.self_attn.q_proj.weight', 'text_model.encoder.layers.5.self_attn.v_proj.weight', 'text_model.encoder.layers.4.layer_norm1.weight', 'text_model.encoder.layers.7.self_attn.k_proj.bias', 'text_model.encoder.layers.5.self_attn.k_proj.weight', 'text_model.encoder.layers.10.mlp.fc2.bias', 'text_model.encoder.layers.8.layer_norm1.bias', 'text_model.encoder.layers.6.self_attn.out_proj.bias', 'text_model.encoder.layers.10.layer_norm2.bias', 'text_model.encoder.layers.1.layer_norm1.bias', 'text_model.encoder.layers.3.self_attn.v_proj.bias', 'text_model.encoder.layers.0.mlp.fc1.bias', 'logit_scale', 'text_model.encoder.layers.5.mlp.fc2.bias', 'text_model.encoder.layers.7.self_attn.out_proj.bias', 'text_model.encoder.layers.6.mlp.fc1.weight', 'text_model.encoder.layers.4.self_attn.k_proj.bias', 'text_model.encoder.layers.6.mlp.fc2.weight', 'text_model.encoder.layers.9.self_attn.out_proj.weight', 'text_model.encoder.layers.1.mlp.fc1.weight', 'text_model.encoder.layers.9.mlp.fc2.bias', 'text_model.encoder.layers.11.self_attn.k_proj.bias', 'text_model.encoder.layers.4.self_attn.q_proj.bias', 'text_model.encoder.layers.2.mlp.fc1.weight', 'text_model.encoder.layers.8.mlp.fc2.bias', 'text_model.encoder.layers.0.self_attn.q_proj.bias', 'text_model.encoder.layers.1.self_attn.v_proj.bias', 'text_model.encoder.layers.7.mlp.fc2.weight', 'text_model.encoder.layers.11.self_attn.out_proj.bias', 'text_model.encoder.layers.3.layer_norm2.weight', 'text_model.encoder.layers.10.mlp.fc1.bias', 'text_model.encoder.layers.10.layer_norm1.weight', 'text_model.encoder.layers.4.layer_norm2.weight', 'text_model.encoder.layers.1.mlp.fc2.bias', 'text_model.encoder.layers.9.self_attn.out_proj.bias', 'text_model.encoder.layers.9.self_attn.v_proj.weight', 'text_model.encoder.layers.5.mlp.fc1.weight', 'text_model.encoder.layers.0.mlp.fc1.weight', 'text_model.encoder.layers.7.layer_norm2.bias', 'text_model.encoder.layers.0.self_attn.out_proj.weight', 'text_model.encoder.layers.5.self_attn.v_proj.bias', 'text_model.encoder.layers.10.mlp.fc2.weight', 'text_model.embeddings.position_ids', 'text_model.encoder.layers.4.self_attn.out_proj.weight', 'text_model.encoder.layers.2.self_attn.k_proj.bias', 'text_model.encoder.layers.0.layer_norm1.bias', 'text_model.encoder.layers.4.self_attn.q_proj.weight', 'text_model.encoder.layers.8.layer_norm2.weight', 'text_model.encoder.layers.1.layer_norm2.bias', 'text_model.encoder.layers.1.self_attn.k_proj.bias', 'text_model.encoder.layers.0.self_attn.out_proj.bias', 'text_model.encoder.layers.6.self_attn.k_proj.weight', 'text_model.encoder.layers.4.self_attn.v_proj.weight', 'text_model.encoder.layers.9.self_attn.k_proj.bias', 'visual_projection.weight', 'text_model.encoder.layers.2.self_attn.v_proj.bias', 'text_model.encoder.layers.10.self_attn.out_proj.bias', 'text_model.encoder.layers.11.mlp.fc2.bias', 'text_model.encoder.layers.5.mlp.fc1.bias', 'text_model.encoder.layers.3.self_attn.out_proj.bias', 'text_model.encoder.layers.2.layer_norm1.weight', 'text_model.encoder.layers.11.mlp.fc1.weight', 'text_model.encoder.layers.10.self_attn.q_proj.bias', 'text_model.encoder.layers.4.self_attn.out_proj.bias', 'text_model.encoder.layers.4.layer_norm2.bias', 'text_model.encoder.layers.0.self_attn.q_proj.weight', 'text_model.encoder.layers.0.layer_norm2.bias', 'text_model.encoder.layers.3.layer_norm2.bias', 'text_model.encoder.layers.6.self_attn.v_proj.weight', 'text_model.encoder.layers.6.self_attn.k_proj.bias', 'text_model.embeddings.position_embedding.weight', 'text_model.encoder.layers.3.mlp.fc2.bias', 'text_model.encoder.layers.10.self_attn.v_proj.weight', 'text_model.encoder.layers.6.layer_norm1.weight', 'text_model.encoder.layers.11.self_attn.v_proj.weight', 'text_model.encoder.layers.8.mlp.fc1.weight', 'text_model.encoder.layers.9.mlp.fc1.weight', 'text_model.encoder.layers.7.self_attn.v_proj.weight', 'text_model.encoder.layers.2.mlp.fc2.weight', 'text_model.encoder.layers.0.mlp.fc2.bias', 'text_model.encoder.layers.8.self_attn.v_proj.weight', 'text_model.encoder.layers.1.mlp.fc1.bias', 'text_model.encoder.layers.2.layer_norm2.bias', 'text_model.encoder.layers.2.layer_norm1.bias', 'text_model.encoder.layers.7.layer_norm1.bias', 'text_model.encoder.layers.9.layer_norm2.weight', 'text_model.encoder.layers.8.self_attn.q_proj.bias', 'text_model.encoder.layers.9.mlp.fc1.bias', 'text_model.encoder.layers.9.mlp.fc2.weight', 'text_model.encoder.layers.11.self_attn.out_proj.weight', 'text_model.encoder.layers.8.mlp.fc2.weight', 'text_model.encoder.layers.3.self_attn.q_proj.bias', 'text_model.encoder.layers.10.self_attn.out_proj.weight', 'text_model.encoder.layers.3.self_attn.v_proj.weight', 'text_model.encoder.layers.3.mlp.fc1.bias', 'text_model.encoder.layers.4.layer_norm1.bias', 'text_model.encoder.layers.0.self_attn.v_proj.bias', 'text_model.final_layer_norm.bias', 'text_model.encoder.layers.8.self_attn.q_proj.weight', 'text_model.encoder.layers.6.layer_norm1.bias', 'text_model.encoder.layers.6.self_attn.q_proj.weight', 'text_model.encoder.layers.11.layer_norm2.weight', 'text_model.embeddings.token_embedding.weight', 'text_model.encoder.layers.2.self_attn.q_proj.bias', 'text_model.encoder.layers.2.mlp.fc2.bias', 'text_model.encoder.layers.10.self_attn.k_proj.bias', 'text_model.encoder.layers.11.layer_norm2.bias', 'text_model.encoder.layers.8.mlp.fc1.bias', 'text_model.encoder.layers.7.layer_norm2.weight', 'text_model.encoder.layers.2.self_attn.out_proj.weight']

• This IS expected if you are initializing CLIPVisionModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
• This IS NOT expected if you are initializing CLIPVisionModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
  ╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
  │ in <cell line: 3>:3 │
  │ │

│ /content/llava/llava_eval.py:39 in llava_generate │
│ │
│ 36 │ tokenizer = AutoTokenizer.from_pretrained(model_name) │
│ 37 │ │
│ 38 │ if "mpt" in model_name.lower(): │
│ ❱ 39 │ │ model = LlavaMPTForCausalLM.from_pretrained(model_name, low_cpu_mem_usage=True, │
│ 40 │ else: │
│ 41 │ │ model = LlavaLlamaForCausalLM.from_pretrained(model_name, low_cpu_mem_usage=True │
│ 42 │ image_processor = CLIPImageProcessor.from_pretrained(model.config.mm_vision_tower, t │
│ │
│ /usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py:2693 in from_pretrained │
│ │
│ 2690 │ │ │ ) │
│ 2691 │ │ │ │
│ 2692 │ │ │ if model._no_split_modules is None: │
│ ❱ 2693 │ │ │ │ raise ValueError(f"{model.class.name} does not support device_m │ │ 2694 │ │ │ no_split_modules = model._no_split_modules │ │ 2695 │ │ │ if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]: │ │ 2696 │ │ │ │ raise ValueError( │ ╰──────────────────────────────────────────────────────────────────────────────────────────────────╯ ValueError: LlavaMPTForCausalLM does not support device_map='auto'` yet.

is there any way to solve this and load the model with device map as auto to utilise GPU, CPU and Disk.

@liuhaotian look down here!